Treebanks are important linguistic resources, which are structured and annotated corpora with rich linguistic annotations. These resources are used in Natural Language Processing (NLP) applications, supporting linguistic analyses, and are essential for training and evaluating various computational models. This paper discusses the creation of Tamil treebanks using three distinct approaches: manual annotation, computational grammars, and machine learning techniques. Manual annotation, though time-consuming and requiring linguistic expertise, ensures high-quality and rich syntactic and semantic information. Computational deep grammars, such as Lexical Functional Grammar (LFG), offer deep linguistic analyses but necessitate significant knowledge of the formalism. Machine learning approaches, utilising off-the-shelf frameworks and tools like Stanza, UDpipe, and UUParser, facilitate the automated annotation of large datasets but depend on the availability of quality annotated data, cross-linguistic training resources, and computational power. The paper discusses the challenges encountered in building Tamil treebanks, including issues with Internet data, the need for comprehensive linguistic analysis, and the difficulty of finding skilled annotators. Despite these challenges, the development of Tamil treebanks is essential for advancing linguistic research and improving NLP tools for Tamil.

As part of a regular series powered by IBM, BetaKit interviews Canadian tech leaders using innovation for the greater good. In the environmental tech space, common missions revolve around reducing pollution, fixing destroyed habitats, or making the environment better. Less commonly will you find a startup that wants the world to spend less money on solutions, but that's exactly what Ambience Data hopes to do. The company's internet-of-things (IoT) sensors provide hyperlocal air quality data and, through machine-learning algorithms on the cloud, aid in urban development strategy. Instead of trying to fix environmental problems, co-founder and CEO Nisha Sarveswaran hopes that Ambience Data can turn the conversation to investing in prevention.